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          雷达杂波处理
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        <h1 id="雷达杂波处理-Clutter-Process"><a href="#雷达杂波处理-Clutter-Process" class="headerlink" title="雷达杂波处理(Clutter Process)"></a>雷达杂波处理(Clutter Process)</h1><h2 id="固定杂波阈值分割"><a href="#固定杂波阈值分割" class="headerlink" title="固定杂波阈值分割"></a>固定杂波阈值分割</h2><ul>
<li><p>优点：原理简单实现难度低</p>
</li>
<li><p>缺点：阈值门限难以确定，阈值设置的过高，虚警（false alarms）会降低，但同时也会掩盖有效的目标，阈值门限设置的过低，则会导致过多的错误警报。</p>
</li>
</ul>
<h2 id="去除0多普勒速度信号（将回波信号中具有0多普勒速度的信号去除）"><a href="#去除0多普勒速度信号（将回波信号中具有0多普勒速度的信号去除）" class="headerlink" title="去除0多普勒速度信号（将回波信号中具有0多普勒速度的信号去除）"></a>去除0多普勒速度信号（将回波信号中具有0多普勒速度的信号去除）</h2><ul>
<li><p>优点：原理简单，实现难度低，由于场景中的杂波往往是由静止目标产生，0多普勒滤波能有效地去除这些杂波。</p>
</li>
<li><p>缺点：此方法会导致雷达不能检测场景中的静态目标，这将导致检测失败。</p>
</li>
</ul>
<a id="more"></a>
<h2 id="动态阈值分割（通过场景环境变化动态改变阈值，实现恒定的虚警率）"><a href="#动态阈值分割（通过场景环境变化动态改变阈值，实现恒定的虚警率）" class="headerlink" title="动态阈值分割（通过场景环境变化动态改变阈值，实现恒定的虚警率）"></a>动态阈值分割（通过场景环境变化动态改变阈值，实现恒定的虚警率）</h2><h3 id="均值类CFAR"><a href="#均值类CFAR" class="headerlink" title="均值类CFAR"></a>均值类CFAR</h3><ul>
<li><p>Cell Averaging(CA-CFAR)单元平均</p>
<ul>
<li>优点：检测阈值能够随干扰噪声电平的变化动态调节，从而保持CFAR</li>
<li>局限：单元平均CFAR主要基于以下两个假设：</li>
</ul>
</li>
</ul>
<ol>
<li>目标是是独立的。具体是，目标间至少分开一个参考窗的长度，以使得参考窗里不会有存在两个目标的可能。</li>
<li>参考窗里的所有干扰数据样本是独立同分布的，且和包含目标的单元内的干扰同分布，换句话说，干扰是均匀的。<br>当存在两个或两个以上的目标，且一个目标位于待检测单元，而其它一个或多个目标位于参考单元里的时候，会出现目标遮蔽现象。假设位于检测单元里的目标回波功率超过了周围干扰的功率，那么它的存在就会提高对干扰功率的估计值，进而提高CFAR的检测阈值。参考单元里的目标可能会遮蔽待检测单元里的目标。</li>
</ol>
<ul>
<li><p>(SOCA CFAR)单元平均选小<br>在N个单元的SOCA CFAR处理中，对前后参考窗的数据分别进行平均处理，得到两个独立的噪声估计值，两次估计均基于N/2个单元，然后选用两个估计值之中较小的那个作为对干扰功率的估计值。</p>
<ul>
<li>优点：其主要解决了在CA-CFAR处理中，由于参考单元里存在干扰目标时所引起的目标遮蔽效应。</li>
<li>缺点：由于其判断逻辑确保了检测阈值的值是基于较低干扰功率参考窗计算得到的，这样就极大增加了待检测单元的杂波超过阈值的概率。这种现象在处理杂波边缘时表现的尤为显著。</li>
</ul>
</li>
<li><p>(GOCA CFAR)单元平均选大<br>在N个单元的SOCA CFAR处理中，对前后参考窗的数据分别进行平均处理，得到两个独立的噪声估计值，两次估计均基于N/2个单元，然后选用两个估计值之中较大的那个作为对干扰功率的估计值。</p>
<ul>
<li>优点：对于不大可能出现紧邻目标，但杂波严重不均匀的场合，则相对目标遮蔽效应，应该更加关注杂波边缘的虚警。采用单元平均选大策略可以很好的在杂波边缘处避免了虚警。</li>
<li>缺点：与单元平均选小策略想对应，在杂波边缘时，由于选用的是基于较高干扰功率参考窗计算得到的阈值，可能会使弱目标漏检。</li>
</ul>
</li>
</ul>
<h3 id="Ordered-Statistics-OS-CFAR-有序统计"><a href="#Ordered-Statistics-OS-CFAR-有序统计" class="headerlink" title="Ordered Statistics(OS-CFAR)有序统计"></a>Ordered Statistics(OS-CFAR)有序统计</h3><p>保留了CA-CFAR算法使用的一维或二维滑窗结构，如果需要也可以使用保护单元，但是彻底摒弃了后者通过参考单元的数据进行平均，从而直接估计干扰功率电平的方法。取而代之的是，OS-CFAR对参考数据进行一个排序，形成一个升序排列的新序列。排完序的序列的第k个元素称为第k个有序统计量。OS-CFAR选取第k个有序统计量的值作为干扰功率电平的估计，并设阈值为该值和一个因子的乘积。</p>
<ul>
<li>优点：抑制遮蔽效应引起的性能恶化。</li>
<li>缺点：干扰实际上是从一个样本估计中得到，k值的选取与最后相乘因子的选取尤为重要。<br>一般来讲，若k&lt;N/2，则在杂波边缘处会出现较多的虚警，因此，k的取值一般要满足N/2 &lt;k &lt;N的条件。通常k的取值在0.75N附近。</li>
</ul>
<h3 id="multiple-variants-of-CA-CFAR分布自由的CFAR算法或称非参量CFAR（DF-CFAR）"><a href="#multiple-variants-of-CA-CFAR分布自由的CFAR算法或称非参量CFAR（DF-CFAR）" class="headerlink" title="multiple variants of CA-CFAR分布自由的CFAR算法或称非参量CFAR（DF CFAR）"></a>multiple variants of CA-CFAR分布自由的CFAR算法或称非参量CFAR（DF CFAR）</h3><p>未完待续…</p>

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